I’m dealing with a Cassandra setup where I have two tables. One table stores log data with a timestamp as the partition key and a parameter as the clustering key. It gets about 10,000 rows per partition every second.
The other table has info about user-defined datasets. It uses the dataset name as the partition key and has timestamps that match the first table as a clustering column.
I know joining these tables isn’t ideal for Cassandra, but I’m using Spark SQL to do it. My query looks like this:
SELECT * FROM datasets JOIN data
WHERE data.timestamp = datasets.timestamp AND datasets.name = 'my_dataset'
What I’m wondering is: Does Spark SQL know to only read the partitions from the data table that match the timestamps in the datasets table? Or will it read all the data and then filter it?
I’m trying to figure out if this approach is efficient or if I need to rethink my strategy. Any insights on how Spark SQL handles these kinds of joins with Cassandra would be really helpful!
Spark SQL’s optimization capabilities with Cassandra joins are limited, as it may not fully leverage Cassandra’s partition structure and might end up reading more data than necessary. To improve efficiency, you can use Spark’s explain() to analyze the query execution plan and pre-filter the datasets table to reduce unnecessary data transfer. Additionally, leveraging the DataStax Spark Cassandra Connector, implementing data denormalization or pre-aggregation in Cassandra, adjusting the data model to better match query patterns, and caching intermediate results in Spark could all contribute to better performance.
Monitor query performance and experiment with these approaches to find the most efficient solution for your specific use case.
hey there! spark SQL’s pretty clever, but it might not always optimize cassandra joins perfectly. have u tried using spark’s explain() function? it could give u some cool insights into how ur query’s being executed. also, maybe consider pre-filtering that datasets table before joining? just a thought! what other strategies have u tried so far?
spark SQL’s smart, but it might not fully optimize cassandra joins. it could still read more data than needed. consider pre-filtering datasets table or caching results for better performance. maybe try spark’s explain() to see query plan. could also look into DataStax connector optimizations if ur using it.